The purpose of this document is to report upon the initial independent implementation and testing of the Bayesian Markov Chain Monte Carlo (MCMC) algorithm Differential Evolution Markov Chain (DE-MC). To support the US Army Corps of Engineers (USACE) use of risk-based analysis in flood damage reduction studies, Skahill (2012) identified the most promising state-ofthe- art and practice-oriented approaches to robustly quantify hydrologic and hydraulic (H&H) model uncertainty. Skahill (2012) provides a path forward for related work activities, including software development, preparation of practice-oriented guidance documentation, and research and development directed at improving uncertainty analysis algorithm efficiency. Bayesian Markov Chain Monte Carlo (MCMC), and in particular DiffeRential Evolution Adaptive Metropolis (DREAM) (Vrugt et al. 2008a, 2009), and/or its basis, Differential Evolution Markov Chain (DE-MC) (ter Braak 2006), was selected by Skahill (2012) as the state-of-the-art method for estimating model parameter and predictive uncertainty. The intent of MCMC is to sample (upon completion of the burn-in period), via stochastic simulation, from the noted target equilibrium (i.e., posterior probability) distribution. The purpose of this document is to report upon the initial independent implementation and testing of the Bayesian MCMC algorithm DE-MC. This new DEMC implementation differs notably from other MCMC implementations in that additional sampler burn-in (burn-in is the initial period when the MCMC sampler has not yet converged to its target equilibrium distribution) assessment heuristics were incorporated into the algorithm. These heuristics attempt to support a more robust assessment of sampler burn-in, rather than solely relying upon a quantitative sampler convergence diagnostic which can frequently prematurely misdiagnose convergence to the equilibrium target distribution.